'''simple docstring'''




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase					="▁"

__UpperCAmelCase					={"vocab_file": "spiece.model"}

__UpperCAmelCase					={
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

__UpperCAmelCase					={
    "google/pegasus-xsum": 5_1_2,
}


__UpperCAmelCase					=logging.get_logger(__name__)




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				=VOCAB_FILES_NAMES

     lowerCamelCase    :	int				=VOCAB_FILES_NAMES
     lowerCamelCase    :	str				=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase    :	List[Any]				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase    :	Union[str, Any]				=["input_ids", "attention_mask"]






     def __init__(					self						:						Tuple    ,							a						:						Any    ,							a						:						Optional[Any]="<pad>"    ,							a						:						int="</s>"    ,							a						:						Dict="<unk>"    ,							a						:						Tuple="<mask_2>"    ,							a						:						Any="<mask_1>"    ,							a						:						Optional[int]=None    ,							a						:						Optional[Any]=1_03    ,							a						:						Optional[Dict[str, Any]] = None    ,							**a						:						List[Any]    ,							):





         """simple docstring"""



         __lowerCamelCase            =    offset
         if additional_special_tokens is not None:
             if not isinstance(a    ,							a       ):
                 raise TypeError(
                     f"""additional_special_tokens should be of type {type(a       )}, but is"""
                     f""" {type(a       )}"""       )

             __lowerCamelCase            =    (
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 f"""<unk_{i}>""" for i in range(len(a       )    ,							self.offset - 1       )
             ]

             if len(set(a       )       ) != len(a       ):
                 raise ValueError(
                     '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                     f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
             __lowerCamelCase            =    additional_special_tokens_extended
         else:
             __lowerCamelCase            =    [mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [f"""<unk_{i}>""" for i in range(2    ,							self.offset       )]

         __lowerCamelCase            =    {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=a    ,							unk_token=a    ,							mask_token=a    ,							pad_token=a    ,							mask_token_sent=a    ,							offset=a    ,							additional_special_tokens=a    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a    ,							)
         __lowerCamelCase            =    mask_token_sent
         __lowerCamelCase            =    vocab_file
         __lowerCamelCase            =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(a       )

         # add special tokens to encoder dict
         __lowerCamelCase            =    {
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }       )

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1    ,							self.offset - 1       )}       )

         __lowerCamelCase            =    {v: k for k, v in self.encoder.items()}






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         return len(self.sp_model       ) + self.offset






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    {self.convert_ids_to_tokens(a       ): i for i in range(self.vocab_size       )}
         vocab.update(self.added_tokens_encoder       )
         return vocab






     def __getstate__(					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.__dict__.copy()
         __lowerCamelCase            =    None
         return state






     def __setstate__(					self						:						Any    ,							a						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    d

         # for backward compatibility
         if not hasattr(self    ,							'''sp_model_kwargs'''       ):
             __lowerCamelCase            =    {}

         __lowerCamelCase            =    spm.SentencePieceProcessor(**self.sp_model_kwargs       )
         self.sp_model.Load(self.vocab_file       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						str       ):





         """simple docstring"""



         return self.sp_model.encode(a    ,							out_type=a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						str       ):





         """simple docstring"""



         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         __lowerCamelCase            =    self.sp_model.piece_to_id(a       )
         return sp_id + self.offset






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						int       ):





         """simple docstring"""



         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             __lowerCamelCase            =    self.sp_model.IdToPiece(index - self.offset       )
         return token






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    []
         __lowerCamelCase            =    ''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(a       ) + token
                 __lowerCamelCase            =    []
             else:
                 current_sub_tokens.append(a       )
         out_string += self.sp_model.decode(a       )
         return out_string.strip()






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						Union[str, Any]=False       ):





         """simple docstring"""



         return 1






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict    ,							a						:						str       ):





         """simple docstring"""



         __lowerCamelCase            =    set(self.all_special_ids       )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List    ,							a						:						Optional[List] = None    ,							a						:						bool = False       ):





         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(a       )
         elif token_ids_a is None:
             return self._special_token_mask(a       ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]    ,							a						:						Union[str, Any]    ,							a						:						int=None       ):





         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]






     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						str    ,							a						:						Optional[str] = None       ):





         """simple docstring"""



         if not os.path.isdir(a       ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
             return
         __lowerCamelCase            =    os.path.join(
             a    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

         if os.path.abspath(self.vocab_file       ) != os.path.abspath(a       ) and os.path.isfile(self.vocab_file       ):
             copyfile(self.vocab_file    ,							a       )
         elif not os.path.isfile(self.vocab_file       ):
             with open(a    ,							'''wb'''       ) as fi:
                 __lowerCamelCase            =    self.sp_model.serialized_model_proto()
                 fi.write(a       )

         return (out_vocab_file,)

'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


__UpperCAmelCase					=logging.get_logger(__name__)

__UpperCAmelCase					={
    "Visual-Attention-Network/van-base": (
        "https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"
    ),
}




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Optional[Any]				="van"






     def __init__(					self						:						Union[str, Any]    ,							a						:						List[Any]=2_24    ,							a						:						Optional[int]=3    ,							a						:						Optional[Any]=[7, 3, 3, 3]    ,							a						:						int=[4, 2, 2, 2]    ,							a						:						Optional[int]=[64, 1_28, 3_20, 5_12]    ,							a						:						str=[3, 3, 12, 3]    ,							a						:						str=[8, 8, 4, 4]    ,							a						:						int="gelu"    ,							a						:						int=0.02    ,							a						:						Optional[int]=1e-6    ,							a						:						List[str]=1e-2    ,							a						:						Union[str, Any]=0.0    ,							a						:						int=0.0    ,							**a						:						Optional[Any]    ,							):





         """simple docstring"""



         super().__init__(**a       )
         __lowerCamelCase            =    image_size
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_sizes
         __lowerCamelCase            =    strides
         __lowerCamelCase            =    hidden_sizes
         __lowerCamelCase            =    depths
         __lowerCamelCase            =    mlp_ratios
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    layer_norm_eps
         __lowerCamelCase            =    layer_scale_init_value
         __lowerCamelCase            =    drop_path_rate
         __lowerCamelCase            =    dropout_rate
